Anti-Th/To Are Common Antinucleolar Autoantibodies in Italian Patients with Scleroderma
Bibliographic record
Abstract
OBJECTIVE: Patients with scleroderma (systemic sclerosis; SSc) can be classified into subsets based on autoantibody profile and clinical features. Specificities such as anti-Th/To and anti-fibrillarin (U3RNP) are detectable mainly by immunoprecipitation (IP), which is not widely used in clinical laboratories. We examined the autoantibody profiles and clinical manifestations in a cohort of Italian patients with SSc, focusing on anti-Th/To and anticentromere (ACA) antibodies, associated with limited cutaneous SSc (lcSSc). METHODS: Sera from 216 consecutive patients with SSc were tested for ACA (by indirect immunofluorescence), antitopoisomerase I (topo I; by counterimmunoelectrophoresis), and anti-RNA polymerase III (RNAPIII; by ELISA). Forty-one sera negative for these specificities were tested by IP analysis of proteins ((35)S-methionine labeled K562 cell extract) and RNA (silver staining). RESULTS: Among 216 SSc patients analyzed, anti-topo I, ACA, and anti-RNAPIII were detected in 38% (81/216), 31% (67/216) and 7% (15/216), respectively. Among 41 sera negative for ACA, anti-topo I, and anti-RNAPIII and which were tested by IP, 14 were nucleolar stain-positive. Eight out of 14 (57%) showed anti-Th/To reactivity, but no anti-U3RNP was found. In comparison with ACA-positive patients, anti-Th/To-positive patients were younger (p = 0.0046) and more commonly were male (p = 0.0006). All 8 anti-Th/To-positive and all but one ACA-positive patients had lcSSc. Interstitial lung disease (ILD) and pericarditis were more frequent in anti-Th/To-positive patients. CONCLUSION: Anti-Th/To are common in antinucleolar antibody-positive Italian patients with SSc. Anti-Th/To and ACA patients had lcSSc, with excellent prognosis. The anti-Th/To group had frequent pericarditis and ILD, although impairment of pulmonary function appeared mild.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".